{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch Normalization\n",
    "One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. One idea along these lines is batch normalization which was recently proposed by [3].\n",
    "\n",
    "The idea is relatively straightforward. Machine learning methods tend to work better when their input data consists of uncorrelated features with zero mean and unit variance. When training a neural network, we can preprocess the data before feeding it to the network to explicitly decorrelate its features; this will ensure that the first layer of the network sees data that follows a nice distribution. However even if we preprocess the input data, the activations at deeper layers of the network will likely no longer be decorrelated and will no longer have zero mean or unit variance since they are output from earlier layers in the network. Even worse, during the training process the distribution of features at each layer of the network will shift as the weights of each layer are updated.\n",
    "\n",
    "The authors of [3] hypothesize that the shifting distribution of features inside deep neural networks may make training deep networks more difficult. To overcome this problem, [3] proposes to insert batch normalization layers into the network. At training time, a batch normalization layer uses a minibatch of data to estimate the mean and standard deviation of each feature. These estimated means and standard deviations are then used to center and normalize the features of the minibatch. A running average of these means and standard deviations is kept during training, and at test time these running averages are used to center and normalize features.\n",
    "\n",
    "It is possible that this normalization strategy could reduce the representational power of the network, since it may sometimes be optimal for certain layers to have features that are not zero-mean or unit variance. To this end, the batch normalization layer includes learnable shift and scale parameters for each feature dimension.\n",
    "\n",
    "[3] Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n",
    "Internal Covariate Shift\", ICML 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train:  (49000, 3, 32, 32)\n",
      "y_train:  (49000,)\n",
      "X_val:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "  print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: Forward\n",
    "In the file `cs231n/layers.py`, implement the batch normalization forward pass in the function `batchnorm_forward`. Once you have done so, run the following to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before batch normalization:\n",
      "  means:  [ -2.3814598  -13.18038246   1.91780462]\n",
      "  stds:  [ 27.18502186  34.21455511  37.68611762]\n",
      "After batch normalization (gamma=1, beta=0)\n",
      "  mean:  [  2.88657986e-17   3.66373598e-17  -1.32532874e-17]\n",
      "  std:  [ 0.99618489  0.93315414  0.99870767]\n",
      "After batch normalization (nontrivial gamma, beta)\n",
      "  means:  [ 11.  12.  13.]\n",
      "  stds:  [ 0.99618489  1.86630827  2.996123  ]\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after batch normalization\n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print('Before batch normalization:')\n",
    "print('  means: ', a.mean(axis=0))\n",
    "print('  stds: ', a.std(axis=0))\n",
    "\n",
    "# Means should be close to zero and stds close to one\n",
    "print('After batch normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = batchnorm_forward(a, np.ones(D3), np.zeros(D3), {'mode': 'train'})\n",
    "print('  mean: ', a_norm.mean(axis=0))\n",
    "print('  std: ', a_norm.std(axis=0))\n",
    "\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "gamma = np.asarray([1.0, 2.0, 3.0])\n",
    "beta = np.asarray([11.0, 12.0, 13.0])\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print('After batch normalization (nontrivial gamma, beta)')\n",
    "print('  means: ', a_norm.mean(axis=0))\n",
    "print('  stds: ', a_norm.std(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After batch normalization (test-time):\n",
      "  means:  [-0.03917443 -0.04330376 -0.10420588]\n",
      "  stds:  [ 1.01275215  1.00801307  0.97519584]\n"
     ]
    }
   ],
   "source": [
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "for t in range(50):\n",
    "  X = np.random.randn(N, D1)\n",
    "  a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "  batchnorm_forward(a, gamma, beta, bn_param)\n",
    "bn_param['mode'] = 'test'\n",
    "X = np.random.randn(N, D1)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print('After batch normalization (test-time):')\n",
    "print('  means: ', a_norm.mean(axis=0))\n",
    "print('  stds: ', a_norm.std(axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch Normalization: backward\n",
    "Now implement the backward pass for batch normalization in the function `batchnorm_backward`.\n",
    "\n",
    "To derive the backward pass you should write out the computation graph for batch normalization and backprop through each of the intermediate nodes. Some intermediates may have multiple outgoing branches; make sure to sum gradients across these branches in the backward pass.\n",
    "\n",
    "Once you have finished, run the following to numerically check your backward pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  1.70292611676e-09\n",
      "dgamma error:  7.42041421625e-13\n",
      "dbeta error:  2.87950576558e-12\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "np.random.seed(231)\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: batchnorm_forward(x, a, beta, bn_param)[0]\n",
    "fb = lambda b: batchnorm_forward(x, gamma, b, bn_param)[0]\n",
    "\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n",
    "\n",
    "_, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = batchnorm_backward(dout, cache)\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch Normalization: alternative backward (OPTIONAL, +3 points extra credit)\n",
    "In class we talked about two different implementations for the sigmoid backward pass. One strategy is to write out a computation graph composed of simple operations and backprop through all intermediate values. Another strategy is to work out the derivatives on paper. For the sigmoid function, it turns out that you can derive a very simple formula for the backward pass by simplifying gradients on paper.\n",
    "\n",
    "Surprisingly, it turns out that you can also derive a simple expression for the batch normalization backward pass if you work out derivatives on paper and simplify. After doing so, implement the simplified batch normalization backward pass in the function `batchnorm_backward_alt` and compare the two implementations by running the following. Your two implementations should compute nearly identical results, but the alternative implementation should be a bit faster.\n",
    "\n",
    "NOTE: This part of the assignment is entirely optional, but we will reward 3 points of extra credit if you can complete it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx difference:  8.44535262644e-13\n",
      "dgamma difference:  0.0\n",
      "dbeta difference:  0.0\n",
      "speedup: 1.79x\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D = 100, 500\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "out, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "t1 = time.time()\n",
    "dx1, dgamma1, dbeta1 = batchnorm_backward(dout, cache)\n",
    "t2 = time.time()\n",
    "dx2, dgamma2, dbeta2 = batchnorm_backward_alt(dout, cache)\n",
    "t3 = time.time()\n",
    "\n",
    "print('dx difference: ', rel_error(dx1, dx2))\n",
    "print('dgamma difference: ', rel_error(dgamma1, dgamma2))\n",
    "print('dbeta difference: ', rel_error(dbeta1, dbeta2))\n",
    "print('speedup: %.2fx' % ((t2 - t1) / (t3 - t2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fully Connected Nets with Batch Normalization\n",
    "Now that you have a working implementation for batch normalization, go back to your `FullyConnectedNet` in the file `cs2312n/classifiers/fc_net.py`. Modify your implementation to add batch normalization.\n",
    "\n",
    "Concretely, when the flag `use_batchnorm` is `True` in the constructor, you should insert a batch normalization layer before each ReLU nonlinearity. The outputs from the last layer of the network should not be normalized. Once you are done, run the following to gradient-check your implementation.\n",
    "\n",
    "HINT: You might find it useful to define an additional helper layer similar to those in the file `cs231n/layer_utils.py`. If you decide to do so, do it in the file `cs231n/classifiers/fc_net.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.28617856578\n",
      "W1 relative error: 5.66e-05\n",
      "W2 relative error: 6.02e-06\n",
      "W3 relative error: 3.01e-10\n",
      "b1 relative error: 2.22e-08\n",
      "b2 relative error: 5.55e-09\n",
      "b3 relative error: 1.69e-10\n",
      "beta1 relative error: 3.73e-09\n",
      "beta2 relative error: 5.92e-09\n",
      "gamma1 relative error: 9.41e-09\n",
      "gamma2 relative error: 3.79e-08\n",
      "\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  7.00323626844\n",
      "W1 relative error: 2.42e-06\n",
      "W2 relative error: 1.58e-06\n",
      "W3 relative error: 1.34e-08\n",
      "b1 relative error: 5.55e-09\n",
      "b2 relative error: 1.11e-08\n",
      "b3 relative error: 1.77e-10\n",
      "beta1 relative error: 6.63e-09\n",
      "beta2 relative error: 5.47e-08\n",
      "gamma1 relative error: 6.42e-09\n",
      "gamma2 relative error: 9.92e-08\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for reg in [0, 3.14]:\n",
    "  print('Running check with reg = ', reg)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64,\n",
    "                            use_batchnorm=True)\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print('Initial loss: ', loss)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n",
    "  if reg == 0: print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batchnorm for deep networks\n",
    "Run the following to train a six-layer network on a subset of 1000 training examples both with and without batch normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 200) loss: 2.303344\n",
      "(Epoch 0 / 10) train acc: 0.116000; val_acc: 0.103000\n",
      "(Epoch 1 / 10) train acc: 0.175000; val_acc: 0.154000\n",
      "(Epoch 2 / 10) train acc: 0.222000; val_acc: 0.182000\n",
      "(Epoch 3 / 10) train acc: 0.251000; val_acc: 0.178000\n",
      "(Epoch 4 / 10) train acc: 0.263000; val_acc: 0.175000\n",
      "(Epoch 5 / 10) train acc: 0.282000; val_acc: 0.178000\n",
      "(Epoch 6 / 10) train acc: 0.293000; val_acc: 0.164000\n",
      "(Epoch 7 / 10) train acc: 0.339000; val_acc: 0.198000\n",
      "(Epoch 8 / 10) train acc: 0.343000; val_acc: 0.202000\n",
      "(Epoch 9 / 10) train acc: 0.375000; val_acc: 0.216000\n",
      "(Epoch 10 / 10) train acc: 0.333000; val_acc: 0.183000\n",
      "(Iteration 1 / 200) loss: 2.302585\n",
      "(Epoch 0 / 10) train acc: 0.107000; val_acc: 0.102000\n",
      "(Epoch 1 / 10) train acc: 0.107000; val_acc: 0.102000\n",
      "(Epoch 2 / 10) train acc: 0.107000; val_acc: 0.102000\n",
      "(Epoch 3 / 10) train acc: 0.167000; val_acc: 0.148000\n",
      "(Epoch 4 / 10) train acc: 0.163000; val_acc: 0.148000\n",
      "(Epoch 5 / 10) train acc: 0.173000; val_acc: 0.157000\n",
      "(Epoch 6 / 10) train acc: 0.160000; val_acc: 0.143000\n",
      "(Epoch 7 / 10) train acc: 0.167000; val_acc: 0.137000\n",
      "(Epoch 8 / 10) train acc: 0.190000; val_acc: 0.161000\n",
      "(Epoch 9 / 10) train acc: 0.186000; val_acc: 0.156000\n",
      "(Epoch 10 / 10) train acc: 0.205000; val_acc: 0.164000\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [100, 100, 100, 100, 100]\n",
    "\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 1e-3\n",
    "bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=True)\n",
    "model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=False)\n",
    "\n",
    "bn_solver = Solver(bn_model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=200)\n",
    "bn_solver.train()\n",
    "\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=200)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following to visualize the results from two networks trained above. You should find that using batch normalization helps the network to converge much faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ceA1mFv37/frbmx4K3WcuLP0ti7S8ZnEG03EGbVGO9Tuf8xwctlsVM43CR+20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hQ45wrrniRQufw/4AeBYaLnAK8cZkwiItJCKKETEZFD9RK+Mnct8IFz7rugfa8C04F0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\nyoOXdSS5drzvWCJSRdRDJz+1cwM81xMad4KrJ0OUr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TDznUgiwF8+WMrC9Tv4x9WZNKuv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\nOaer21JpnHMbgWwzywg0nQcs8RhJwksWcJqZ1Qz8e3keWnRHKt9k4JrA/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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f401649c828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.plot(solver.loss_history, 'o', label='baseline')\n",
    "plt.plot(bn_solver.loss_history, 'o', label='batchnorm')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.plot(solver.train_acc_history, '-o', label='baseline')\n",
    "plt.plot(bn_solver.train_acc_history, '-o', label='batchnorm')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.plot(solver.val_acc_history, '-o', label='baseline')\n",
    "plt.plot(bn_solver.val_acc_history, '-o', label='batchnorm')\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch normalization and initialization\n",
    "We will now run a small experiment to study the interaction of batch normalization and weight initialization.\n",
    "\n",
    "The first cell will train 8-layer networks both with and without batch normalization using different scales for weight initialization. The second layer will plot training accuracy, validation set accuracy, and training loss as a function of the weight initialization scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running weight scale 1 / 20\n",
      "Running weight scale 2 / 20\n",
      "Running weight scale 3 / 20\n",
      "Running weight scale 4 / 20\n",
      "Running weight scale 5 / 20\n",
      "Running weight scale 6 / 20\n",
      "Running weight scale 7 / 20\n",
      "Running weight scale 8 / 20\n",
      "Running weight scale 9 / 20\n",
      "Running weight scale 10 / 20\n",
      "Running weight scale 11 / 20\n",
      "Running weight scale 12 / 20\n",
      "Running weight scale 13 / 20\n",
      "Running weight scale 14 / 20\n",
      "Running weight scale 15 / 20\n",
      "Running weight scale 16 / 20\n",
      "Running weight scale 17 / 20\n",
      "Running weight scale 18 / 20\n",
      "Running weight scale 19 / 20\n",
      "Running weight scale 20 / 20\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [50, 50, 50, 50, 50, 50, 50]\n",
    "\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "bn_solvers = {}\n",
    "solvers = {}\n",
    "weight_scales = np.logspace(-4, 0, num=20)\n",
    "for i, weight_scale in enumerate(weight_scales):\n",
    "  print('Running weight scale %d / %d' % (i + 1, len(weight_scales)))\n",
    "  bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=True)\n",
    "  model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=False)\n",
    "\n",
    "  bn_solver = Solver(bn_model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "  bn_solver.train()\n",
    "  bn_solvers[weight_scale] = bn_solver\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "  solver.train()\n",
    "  solvers[weight_scale] = solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RC50GhD0ctfdipWSHvy65dizasvVbAx/Qvq+tvTb9WdhSHMbIVLyJjXeUUiqy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\nLFNKzaqVjH2stX6t+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u+sjui4ZBrU35x5uylAmHsPTJkAEz425chWqknUImJh0mxJ1ITw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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f401a5389e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results of weight scale experiment\n",
    "best_train_accs, bn_best_train_accs = [], []\n",
    "best_val_accs, bn_best_val_accs = [], []\n",
    "final_train_loss, bn_final_train_loss = [], []\n",
    "\n",
    "for ws in weight_scales:\n",
    "  best_train_accs.append(max(solvers[ws].train_acc_history))\n",
    "  bn_best_train_accs.append(max(bn_solvers[ws].train_acc_history))\n",
    "  \n",
    "  best_val_accs.append(max(solvers[ws].val_acc_history))\n",
    "  bn_best_val_accs.append(max(bn_solvers[ws].val_acc_history))\n",
    "  \n",
    "  final_train_loss.append(np.mean(solvers[ws].loss_history[-100:]))\n",
    "  bn_final_train_loss.append(np.mean(bn_solvers[ws].loss_history[-100:]))\n",
    "  \n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Best val accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best val accuracy')\n",
    "plt.semilogx(weight_scales, best_val_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_val_accs, '-o', label='batchnorm')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Best train accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best training accuracy')\n",
    "plt.semilogx(weight_scales, best_train_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_train_accs, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Final training loss vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Final training loss')\n",
    "plt.semilogx(weight_scales, final_train_loss, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_final_train_loss, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "plt.gca().set_ylim(1.0, 3.5)\n",
    "\n",
    "plt.gcf().set_size_inches(10, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question:\n",
    "Describe the results of this experiment, and try to give a reason why the experiment gave the results that it did."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Answer:\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
